CN111947657B - Mobile robot navigation method suitable for compact shelving environment - Google Patents
Mobile robot navigation method suitable for compact shelving environment Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention relates to a mobile robot navigation method suitable for a compact shelving environment. Belongs to the field of mobile robots, and specifically comprises the following steps: (1) acquiring an environment map of the compact bent; (2) positioning the dense bent based on a rectangular detection method; (3) constructing a behavior tree to select a path mode; (4) And generating a planning path between path reference points by using an A-algorithm, tracking the path by using a predictive control method based on a robot kinematic model to realize navigation, and providing a reference by using linear features extracted by local laser observation information under the condition of inaccurate positioning in a long and narrow bent channel. The method realizes the optimal division of the working paths of the robots in the intensive bent environment, eliminates errors caused by manual setting of road mark points, can finish the navigation of various mobile robots in the narrow bent environment, and is suitable for different cruising speeds.
Description
Technical Field
The invention belongs to the field of mobile robots, and particularly relates to a mobile robot navigation method suitable for a compact shelving environment.
Background
When the mobile robot is applied to dense storage environments such as libraries, supermarkets, warehouses and the like with dense and orderly bookshelf/shelf arrangement, various tasks such as checking, inspection and the like are required to be executed, the completion of the tasks is required to realize path planning and control on the robot, the navigation of the mobile robot under different tasks is realized, and the movement of the robot is accurately controlled in a long and narrow bent channel.
At present, most robots working in a compact shelving environment are based on a fixed path tracking method, such as a patent document 'automatic inspection robot for shelf books based on an image recognition technology' (application number CN 201010617824.5), and robot running line mark information stuck on the ground is acquired through a camera/a photosensitive device to carry out line inspection. The patent literature 'a library robot positioning control method' (application number CN 201810402240.2) utilizes RFID tags as road points to establish fuzzy inference rules to realize positioning and navigation. In recent years, positioning navigation of robots is realized based on an SLAM method, such as a laser automatic navigation library inventory robot (application number CN 201910987548.2) in patent literature, multiple navigation waypoints need to be set manually after a map is obtained, time is consumed, the relationship between the waypoints is not subjected to reasoning combination, the risk of navigation failure under a certain target point exists, and optimization and improvement are not performed according to environmental characteristics. How to realize the division and tracking control of the required working paths is a problem to be solved in the navigation of mobile robots in a compact shelving environment.
Disclosure of Invention
Aiming at the problems, the invention provides a mobile robot navigation method suitable for a compact shelving environment.
The technical scheme of the invention is as follows: the mobile robot navigation method suitable for the compact shelving environment comprises the following operation steps:
Step (1.1), acquiring an environment map of the compact bent;
Step (1.2), positioning the dense bent racks based on a rectangular detection method, and generating path reference points;
Step (1.3), constructing a behavior tree reorganization path reference point sequence to generate a global planning path;
And (1.4) controlling the robot to track the planned path based on the model predictive control method.
Further, in the step (1.2), the compact bent refers to: the method comprises the steps of displaying rectangular regular arrangement in an environment map, determining the positions of four corner points of a rectangle through a rectangular detection method, generating path reference points by combining the minimum radius and the safety distance of a robot kinematic model, and usingThe positions of the upper left path reference point, the upper right path reference point, the lower right path reference point, and the lower left path reference point based on the bent number i are indicated, and max is the maximum number of detected rectangles.
Further, the step (1.3) is specifically to construct a behavior tree, select different robot kinematic models, determine sequence combinations of path reference points, and realize switching of different path modes, and preset the following six modes, (a) all the bent frame sequentially patrol frame paths, (b) the two side patrol frame paths of the designated bent frame, (c) the designated bent frame designates a single side patrol frame path, (d) the patrol frame path between the two bent frames, (e) patrol frame paths around the peripheral area of the dense bent frame, (f) the patrol frame paths of a plurality of continuous bent frames from the designated book frame.
Further, the constructed robot kinematics model comprises a two-wheel differential mobile robot model, a three-wheel omni-wheel kinematics model and a four-wheel Mecanum wheel omni-directional kinematics model.
Further, the sequence combination of the path reference points is determined by the position of the robot and the path mode, firstly, the position relation of the position P robot (x, y) of the robot relative to the compact bent frame is determined, the sequence of the direction of the bent frame and the sequence of the path reference points is selected, and secondly, the path reference points related to the bent frame are determined by combining the path mode.
Further, the step (1.4) specifically comprises the steps of controlling the robot to track a global planning path by using a model predictive control method, and obtaining the reference path position and angle from the planning path by sampling; when the position of the robot is in the dense bent area, local laser observation information is used for extracting linear features to represent the bent edge, the reference angle is switched to the angle deviation between the direction of the linear features and the moving direction of the robot, the position x error is artificially set to be 0 under the robot coordinate system, and the position y error is set to be the difference value between the expected edge movement distance and the distance of the linear features;
The prediction model in the model prediction control method is a robot kinematics model.
The beneficial effects of the invention are as follows: the invention realizes the optimal division of the working paths of the mobile robots in the dense bent environment, eliminates errors caused by manual setting of road marking points, provides various working paths meeting tasks such as checking, patrol and the like, and can complete the navigation of various mobile robots in the narrow bent environment and adapt to different cruising speeds compared with the prior art.
Drawings
FIG. 1 is a structural flow diagram of the present invention;
FIG. 2 is a schematic diagram of a behavior tree for constructing a working path partition in the present invention;
FIG. 3 is a schematic block diagram of a control system based on a model predictive method in accordance with the present invention;
FIG. 4 is a global planned path schematic generated in six path modes in the present invention;
FIG. 5 is a comparison of a path smoothly planned using Bezier curves in the present invention;
fig. 6 is a schematic diagram of a preferred mounting location for a twin laser in the present invention.
Detailed Description
In order to more clearly illustrate the technical scheme of the invention, the invention is further described below; it is obvious that the embodiments described below are only a part of examples, and that it will be obvious to those skilled in the art that the technical solutions according to the present invention can be applied to other similar situations according to these without the inventive effort; in order to more clearly describe the technical scheme of the invention, the technical scheme of the invention is further described in detail below with reference to the accompanying drawings:
as shown in fig. 1, a mobile robot navigation method suitable for a compact shelving environment is realized by the following steps,
(1) Acquiring an environment map of the compact bent;
(2) Positioning the dense bent racks based on a rectangular detection method, and generating a path reference point; the method comprises the following steps: the method comprises the steps of displaying rectangular regular arrangement in an environment map, determining the positions of four corner points of a rectangle through a rectangular detection method, generating path reference points by combining the minimum radius and the safety distance of a robot kinematic model, and using The positions of an upper left path reference point, an upper right path reference point, a lower right path reference point and a lower left path reference point based on a bent number i are represented, and max is the maximum number of detected rectangles;
(3) Acquiring a user interaction instruction, dividing a path mode, constructing a behavior tree reorganization path reference point sequence, and generating a global planning path; the method comprises the steps of constructing a behavior tree, selecting different robot kinematic models, determining sequence combinations of path reference points, realizing switching of different path modes, presetting the following six modes, (a), sequentially patrolling all the bent frames to form paths, (b), appointing the two-side bent frame patrolling paths, (c), appointing the bent frame to form a single-side bent frame patrolling path, (d), patrolling paths between the two bent frames, (e), patrolling the bent frame around the peripheral area of the intensive bent frame, and (f), and continuously patrolling a plurality of bent frame patrolling paths from the appointed book frame;
The default inspection direction of the six path modes is Pi(x1,y1)、Pi(x4,y4)、Pi(x3,y3)、Pi(x2,y2), when the position P robot (x, y) of the robot relative to the position of the compact shelving meets the following conditions:
If the condition (1) is marked, the direction of the inspection is opposite, and the inspection is replaced by Pi(x4,y4)、Pi(x1,y1)、Pi(x2,y2)、Pi(x3,y3).
When the position P robot (x, y) of the robot relative to the position of the compact shelving meets the following conditions:
and (2) recording as condition, reordering the sequence of path reference points in reverse order.
Each path mode is that a path reference point sequence is obtained from a sequencer node according to path mode parameters, a left subtree is a condition judging node, a path reference point sequence is loaded with a mark, a right subtree is a selector node, the selection condition is whether a kinematic model is a complete constraint, if yes, the left subtree is executed, an A-type algorithm is sequentially used between the starting position of a robot and the path reference point to generate a global planning path, otherwise, the right subtree is executed, an A-type algorithm is sequentially used between the starting position of the robot and the path reference point sequence, and Bezier curve smooth paths are sequentially used before and after a path control point to generate the global planning path;
(a) All the bent frame sequential inspection paths are default path modes in the state without specified parameters, and the sequence of path reference points is composed as a starting point 、P1(x1,y1)、P1(x4,y4)、P1(x3,y3)、P1(x2,y2)、……、Pi(x1,y1)、Pi(x4,y4)、Pi(x3,y3)、Pi(x2,y2)、……、Pmax(x1,y1)、Pmax(x4,y4)、Pmax(x3,y3)、Pmax(x2,y2);
(B) In the inspection paths on two sides of the appointed bent, a user needs to designate parameters as the appointed bent serial number i, i is more than or equal to 1 and less than or equal to max, and the sequence of the path reference points is used as a starting point 、Pi(x1,y1)、Pi(x4,y4)、Pi(x3,y3)、Pi(x2,y2);
(C) Designating a single-side frame patrol path by designating a frame, wherein the designated parameters are designated frame sequence number i and frame left and right sides, i is more than or equal to 1 and less than or equal to max, left_side flag bit is 1 and represents left side, reference is made to right side when the left_side flag bit is 0, and reference path point sequence composition is a starting point, P i(x1,y1)、Pi(x4,y4) or a starting point and P i(x2,y2)、Pi(x3,y3);
(d) The user needs to specify parameters of a patrol path between two rows of racks, the specified row rack serial number i is more than or equal to 1 and less than or equal to max-1, and the sequence of path reference points is used as a starting point 、Pi(x2,y2)、Pi(x3,y3)、Pi+1(x4,y4)、Pi+1(x1,y1);
(E) Patrol paths are carried out around the peripheral area of the dense bent, the user can designate parameters as designated bent serial numbers i and j, i is more than or equal to 1 and less than or equal to j and less than or equal to max, default parameters are i=1, j=max, and the sequence of path reference points is formed as a starting point 、Pi(x1,y1)、Pi(x4,y4)、Pj(x3,y3)、Pj(x2,y2)、Pi(x1,y1);
(F) A plurality of continuous bent frame patrol paths from a designated book frame, wherein the designated parameters of a user are designated bent frame serial number i and number n, i is more than or equal to 1 and less than or equal to max, n is more than or equal to 0 and less than or equal to max-i, and the sequence of path reference points is used as a starting point 、Pi(x1,y1)、Pi(x4,y4)、Pi(x3,y3)、Pi(x2,y2)、Pi+1(x1,y1)、Pi+1(x4,y4)、Pi+1(x3,y3)、Pi+1(x2,y2)、……、Pi+n(x1,y1)、Pi+n(x4,y4)、Pi+n(x3,y3)、Pi+n(x2,y2);
The constructed robot kinematics model comprises a two-wheel differential mobile robot model, a three-wheel omnidirectional wheel kinematics model and a four-wheel Mecanum wheel omnidirectional kinematics model; the sequence combination of the path reference points is determined by the position of the robot and the path mode, firstly, the position relation of the position P robot (x, y) of the robot relative to the compact bent frame is determined, the sequence of the direction of the bent frame and the sequence of the path reference points is selected, and secondly, the path reference points related to the bent frame are determined by combining the path mode;
(4) And finally, controlling the robot to track the planned path based on a model predictive control method: specifically, a model predictive control method is used for controlling a robot to track a global planning path, a predictive model is a robot kinematics model, and a reference path position and an angle are obtained by sampling from the planning path; when the position of the robot is in a dense bent area, namely the position of the robot is in a rectangular area consisting of P1(x1,y1)、P1(x4,y4)、Pmax(x3,y3)、Pmax(x2,y2), extracting linear characteristics to represent the bent edge by using local laser observation information, switching a reference angle into the angle deviation between the direction of the linear characteristics and the moving direction of the robot, setting a position x error as 0 manually under a robot coordinate system, and setting a position y error as the difference value between the expected edge moving distance and the distance of the linear characteristics;
The prediction model in the model prediction control method is a robot kinematics model.
Setting a robot two-wheel differential mobile robot kinematic model, wherein the starting point is at (8, 92), the minimum radius r_min of the robot kinematic model is 3, the safe distance safe_distance is set to be 1, and the self-adaptive Monte Carlo positioning algorithm is used for providing the robot position information; firstly, establishing an environment grid map through gmapping algorithm, realizing rectangle detection by utilizing opencv library, carrying out edge detection on the grid map by utilizing canny operator, then utilizing findContours function to find out outline, carrying out polygon fitting by utilizing outline points, further judging the maximum cosine of the angle between two adjacent sides under the condition that the outline points are convex quadrilaterals and the area is within a threshold range, obtaining a rectangle when the maximum cosine of the angle between two adjacent sides is smaller than 0.3, taking the coordinates of four corner points of the rectangle under an image coordinate system, converting the coordinates into the map coordinate system, extending the length of the robot to be 4 in the same direction, obtaining a path reference point sequence,Representing positions of an upper left path reference point, an upper right path reference point, a lower right path reference point and a lower left path reference point based on the bent number i, wherein the maximum number max of detected rectangles is 6; judging that the position P robot (x, y) of the robot relative to the position of the compact bent does not meet the condition (1), the direction of the bent is unchanged, Pi(x1,y1)、Pi(x4,y4)、Pi(x3,y3)、Pi(x2,y2), is also not met, and the sequence order of the path reference points is unchanged.
The action tree diagram shown in fig. 2 completes the division of working paths, starting from a root node, a left subtree is a parallel node, the parallel execution of rectangular detection is performed to obtain action nodes of a dense bent path reference point and obtain robot kinematic model action nodes, a right subtree is a selector node for constructing the working paths, and user interaction parameters are obtained, and the action tree diagram enters a corresponding path mode, and comprises the following six steps of (a) sequentially routing all bent frames, (b) designating bent frame paths on two sides, (c) designating bent frame paths, designating single-side bent frame paths, (d) routing between two bent frames, (e) conducting a patrol path around the peripheral area of the dense bent frame, and (f) sequentially routing a plurality of bent frame bent paths from designated book frames.
After the global planning path is generated, a model prediction control-based method is adopted to control the robot to track the planning path, as shown in fig. 3, a control flow chart of the whole system is described; when the robot is in a dense bent area, namely, the self-positioning position of the robot is in a rectangular area consisting of P1(x1,y1)、P1(x4,y4)、Pmax(x3,y3)、Pmax(x2,y2), local laser observation information is used for extracting linear characteristics to represent the bent edge, the reference angle is switched to the angle deviation between the direction of the linear characteristics and the moving direction of the robot, the position x error is set to be 0 artificially under the robot coordinate system, and the position y error is set to be the difference value between the expected edge moving distance and the distance of the linear characteristics.
Taking a kinematic model of a robot as a prediction model, taking a differential mobile robot as an example,
Wherein L is the wheelbase between two wheels; the state space of the model prediction control system is defined as [ x y theta v p e re ], the state space represents the x coordinate, the y coordinate, the angle, the linear speed, the position error and the angle error of the robot position respectively, the position error is represented by the error of a three-time polynomial fitting planning path curve, the reference angle value is obtained by firstly solving the slope of a two-point connecting line and then solving arctan of the two-point connecting line, when the position of the robot is in a dense bent frame area, namely the position of the robot is in a rectangular area consisting of P1(x1,y1)、P1(x4,y4)、Pmax(x3,y3)、Pmax(x2,y2), the linear characteristic is extracted by using local laser observation information to represent the bent frame edge, the reference angle is switched to the angle deviation between the direction of the linear characteristic and the moving direction of the robot, the position x error is artificially set to be 0 under the robot coordinate system, the position y error is set to be the difference value between the expected edge moving distance and the distance of the linear characteristic, and the input quantity of the system is defined as [ v w ], and the input quantity of the system represents the linear speed and the angular speed respectively.
The kinematic model of the three-wheeled omni-wheel mobile robot is that,
Wherein L is the distance from the center of the triangle formed by the three omni-wheels to each wheel.
The four mutually orthogonal Mecanum wheel omnidirectional mobile robot kinematic models are that,
Wherein a is the distance from the center of the robot to the Mecanum wheel on the same side, and b is the distance from the center of the robot to the front end of the Mecanum wheel.
The user selects (a) all the bent frame sequential inspection paths on the interactive interface, and as shown in fig. 4 (a), the planned path generated by the robot in the path mode is shown, and the sequence of the path reference points is formed as the starting point 、P1(x1,y1)、P1(x4,y4)、P1(x3,y3)、P1(x2,y2)、P2(x1,y1)、P2(x4,y4)、P2(x3,y3)、P2(x2,y2)、P3(x1,y1)、P3(x4,y4)、P3(x3,y3)、P3(x2,y2)、P4(x1,y1)、P4(x4,y4)、P4(x3,y3)、P4(x2,y2)、P5(x1,y1)、P5(x4,y4)、P5(x3,y3)、P5(x2,y2)、P6(x1,y1)、P6(x4,y4)、P6(x3,y3)、P6(x2,y2).
The user selects the route of the specified bent at two sides of the interactive interface, the specified parameters are the specified bent sequence 2, as shown in fig. 4 (b), the planned route generated by the robot in the route mode is shown, and the sequence of the route reference points is the starting point 、P2(x1,y1)、P2(x4,y4)、P2(x3,y3)、P2(x2,y2).
The user selects a specified bent specified single-side frame inspection path on the interactive interface, the specified parameter is a specified bent serial number 4, the left_side flag bit is 1, and as shown in fig. 4 (c), a planned path generated by the robot in the path mode is shown, and the sequence of path reference points is a starting point and P 4(x1,y1)、P4(x4,y4.
The user selects a route between two rows of racks on the interactive interface, the designated parameter is designated row number 4, as shown in fig. 4 (d), the planned route generated by the robot in the route mode is shown, and the sequence of the route reference points is the starting point 、P4(x2,y2)、P4(x3,y3)、P5(x4,y4)、P5(x1,y1).
The user selects a patrol path around the peripheral area of the compact bent at the interactive interface, does not specify parameters, uses default parameters i=1, j=max=6, and is a planned path generated by the robot in the path mode as shown in fig. 4 (e), wherein the sequence of path reference points is composed as a starting point 、P1(x1,y1)、P1(x4,y4)、P6(x3,y3)、P6(x2,y2)、P1(x1,y1).
The user selects a plurality of continuous bent inspection routes from a designated book shelf on the interactive interface, the designated bent serial number 3 and the number 2 are shown in the figure 4 (f) which is a planning route generated by the robot in the route mode, and the route reference point sequence is composed as a starting point 、P3(x1,y1)、P3(x4,y4)、P3(x3,y3)、P3(x2,y2)、P4(x1,y1)、P4(x4,y4)、P4(x3,y3)、P4(x2,y2)、P5(x1,y1)、P5(x4,y4)、P5(x3,y3)、P5(x2,y2).
When the dense bent areas have two or more areas, regarding the areas with similar and regular space distribution as one area, combining bent path point information, as shown in fig. 5 (a); the dense bent frames with far space distribution are treated as independent dense bent frame areas according to independent areas, as shown in fig. 5 (b), after a path is generated in the first area at the upper part, the last path point is taken as a robot starting point, and path planning in the second area at the lower part is performed; meanwhile, a planned path of the fully constrained mobile robot is shown in fig. 5 (a).
The laser sensor with 360-degree range is used in the above specific embodiment example; more generally, due to the limitation of appearance packaging, the single laser range installed on the robot does not meet 360 degrees, so that the environmental information around the robot can be collected by double lasers before and after installation, the double laser data are cut and unified under a coordinate system centered by the robot, as shown in fig. 6, the distance from the intersection point C1/C2 of the double laser data to the center of the robot needs to meet the condition of being greater than or equal to the minimum radius of the robot kinematic model, and thus good and stable continuous straight line characteristics can be extracted for feedback guidance of the movement of the robot.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present invention; other variations are possible within the scope of the invention; thus, by way of example, and not limitation, alternative configurations of embodiments of the invention may be considered in keeping with the teachings of the invention; accordingly, the embodiments of the present invention are not limited to the embodiments explicitly described and depicted herein.
Claims (3)
1. A mobile robot navigation method suitable for a compact shelving environment is characterized in that: the operation steps are as follows:
Step (1.1), acquiring an environment map of the compact bent;
Step (1.2), positioning the dense bent racks based on a rectangular detection method, and generating path reference points;
Step (1.3), constructing a behavior tree reorganization path reference point sequence to generate a global planning path;
step (1.4), controlling the robot to track the planned path based on a model predictive control method;
In the step (1.2), the compact bent refers to: the method comprises the steps of displaying rectangular regular arrangement in an environment map, determining the positions of four corner points of a rectangle through a rectangular detection method, generating path reference points by combining the minimum radius and the safety distance of a robot kinematic model, and using The positions of an upper left path reference point, an upper right path reference point, a lower right path reference point and a lower left path reference point based on a bent number i are represented, and max is the maximum number of detected rectangles;
The method comprises the steps of (1.3) constructing a behavior tree, selecting different robot kinematic models, determining sequence combinations of path reference points, and realizing switching of different path modes, wherein six modes are preset, namely (a) sequentially patrol frame paths of all the bent frames, (b) appointed bent frame two-side patrol frame paths, (c) appointed bent frame appointed unilateral patrol frame paths, (d) patrol frame paths between two bent frames, (e) patrol frame paths around peripheral areas of dense bent frames, and (f) continuously patrol frame paths of a plurality of bent frames from appointed book frames;
The sequence combination of the path reference points is determined by the position of the robot and the path mode, firstly, the position relation of the position P robot (x, y) of the robot relative to the compact bent frame is determined, the sequence of the direction of the bent frame and the sequence of the path reference points is selected, and secondly, the path reference points related to the bent frame are determined by combining the path mode.
2. The mobile robot navigation method suitable for a compact shelving environment as defined in claim 1, wherein: the constructed robot kinematics model comprises a two-wheel differential mobile robot model, a three-wheel omnidirectional wheel kinematics model and a four-wheel Mecanum wheel omnidirectional kinematics model.
3. The mobile robot navigation method suitable for a compact shelving environment as defined in claim 1, wherein: the step (1.4) is specifically that a model predictive control method is used for controlling a robot to track a global planning path, and the position and the angle of a reference path are obtained by sampling from the planning path; when the position of the robot is in the dense bent area, local laser observation information is used for extracting linear features to represent the bent edge, the reference angle is switched to the angle deviation between the direction of the linear features and the moving direction of the robot, the position x error is artificially set to be 0 under the robot coordinate system, and the position y error is set to be the difference value between the expected edge movement distance and the distance of the linear features; the prediction model in the model prediction control method is a robot kinematics model.
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